Nex-G Innovations

Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. Machine learning hopes that including the experience into its tasks will eventually improve the learning. The ultimate goal is to improve the learning in such a way that it becomes automatic, so that humans like ourselves don't need to interfere any more.

Code: NES_SK_2255

Duration: 60 Hrs / 6 Weeks / Customized

Mode: Online / Offline / Onsite

+91-8826900551

• Conceptual understanding of learning applications, formulation of learning tasks as computational problems, and methods that are designed to solve these problems
• Understanding the commonalities and differences between different learning tasks and different approaches to learning
• Understanding the trade-offs in developing solutions
• Thorough understanding of rigorously evaluating the performance of learning algorithms
• Ability to manipulate, extend, and apply machine learning methods and algorithms in the context of real-world problems

Module 1 - R Programming Fundamentals

• R Base Software
• Understanding CRAN
• RStudio The IDE
• Sequence of Numbers
• Vectors
• Basic Operations
• Operators and Types
• R Functions

Module 2 - Introduction to Applied Machine Learning

• Why is Machine learning is important?
• Statistical learning vs. Machine learning
• Common Machine Learning Applications
• How Do You Create a Machine Learning Algorithm?
• Iteration and evaluation

Module 3 - Machine Learning with R

• Why use R for machine learning?
• The building of environment on R for machine learning
• Building R studios Platform for machine learning
• Choice of libraries
• Scientific R Packages Overview
• Introduction to R studios and different windows

Module 4 - Introduction to Applied Machine Learning

• Fundamentals of Machine Learning
• Supervised Machine Learning
• Unsupervised Machine Learning
• Semi-supervised Machine Learning
• Evaluation and revalidation
• Applications and beyond

Module 5 - Supervised Machine Learning Models

• How supervised learning algorithms work
• Why and where to use supervised learning
• Concepts of learning models
• Bias-variance tradeoff
• How supervised learning algorithms work
• Algorithms to be used for supervised learning
• Support Vector Machines
• Linear regression
• Logistic regression
• Naive Bayes
• Decision trees
• K-nearest neighbor algorithm
• Challenges of learning
• Exercises

Module 6 - Un-Supervised Machine Learning Models

• How un-supervised learning algorithms work
• Why and where to use unsupervised learning
• Concepts of learning models
• How un-supervised learning algorithms work
• Algorithms to be used for unsupervised learning
• Clustering
• k-Means clustering
• Gaussian mixture models
• Association
• Challenges of learning
• Exercises

Module 7 - Resampling Machine Learning Models

• Cross-validation and the Bootstrap
• Training Error versus Test error
• Training- versus Test-Set Performance
• More on prediction-error estimates
• Validation-set approach
• K-fold Cross-validation
• Exercises

Introduction to Deep Learning

• What is Deep Learning?
• Case studies for deep learning

Practicals

• Implementation of Algorithms on a different dataset
• Working on different phases of the project

Target Audience

• achelor Of Technology, Bachelor Of Engineering, Bachelor Of Science, Bachelor Of Computer Application, Master Of Technology, Master Of Engineering, Master Of Science, Master Of Computer Application.

Training Customization

• Currently Machine learning has varied flavors within the industry and differing adoption strategies for learning are proliferating. To reflect this we can offer a customization service to corporate customers wishing to tailor this series of learning to deliver skills to a wide range of employee roles. Complete blended learning packagers are available that may encompass our online e-learning series, instructor-led classroom technical classes delivered for both onsite and public scheduled formats, and hands-on workshops. We have already delivered Machine Learning training to reputed clients .